Let us take train_SRFBN_example.json as an example.
Note: Before you run python train.py -opt options/train/*.json, please carefully check options: "scale", "dataroot_HR", "dataroot_LR", "networks" and "pretrained_path" (if "pretrain" option is set to "resume" or "finetune").
{
"mode": "sr", // solver type (only "sr" is provided)
"use_cl": true, // whether use multiple losses (required by our SRFBN)
"gpu_ids": [0], // GPU ID to use
"scale": 4, // super resolution scale (*Please carefully check it*)
"is_train": true, // whether train the model
"use_chop": true, // whether enable memory-efficient test
"rgb_range": 255, // maximum value of images
"self_ensemble": false, // whether use self-ensemble strategy for test
"save_image": false, // whether saving visual results during training
// dataset specifications including "train" dataset (for training) and "val" dataset (for validation) (*Please carefully check dateset mode/root*)
"datasets": {
// train dataset
"train": {
"mode": "LRHR", // dataset mode: "LRHR" | "LR", "LRHR" is required during training
"dataroot_HR": "/mnt/data/paper99/DIV2K/Augment/DIV2K_train_HR_aug/x4", // HR dataset root
"dataroot_LR": "/mnt/data/paper99/DIV2K/Augment/DIV2K_train_LR_aug/x4", // LR dataset root
"data_type": "npy", // data type: "img" (image files) | "npy" (binary files), "npy" is recommended for training
"n_workers": 4, // number of threads for data loading
"batch_size": 16, // input batch size
"LR_size": 32, // input (LR) patch size
// data augmentation
"use_flip": true, // whether use horizontal and vertical flips
"use_rot": true, // whether rotate 90 degrees
"noise": "." // noise type: "." (noise free) | "G" (add Gaussian noise) | "S" (add Poisson noise)
},
// validation dataset
"val": {
"mode": "LRHR", // dataset mode: "LRHR" | "LR", "LRHR" is required during validating
"dataroot_HR": "./results/HR/Set5/x4",
"dataroot_LR": "./results/LR/LRBI/Set5/x4",
"data_type": "img"
}
},
// networks specifications
"networks": {
"which_model": "SRFBN", // network name
"num_features": 32, // number of base feature maps
"in_channels": 3, // number of input channels
"out_channels": 3, // number of output channels
"num_steps": 4, // number of time steps (T)
"num_groups": 3 // number of projection groups (G)
},
// solver specifications
"solver": {
"type": "ADAM", // optimizer to use (only "ADAM" is provided)
"learning_rate": 0.0001, // learning rate
"weight_decay": 0, // weight decay
"lr_scheme": "MultiStepLR", // learning rate scheduler (only "MultiStepLR" is provided)
"lr_steps": [200, 400, 600, 800], // milestone for learning rate scheduler
"lr_gamma": 0.5, // gamma for learning rate scheduler
"loss_type": "l1", // loss type: "l1" | "l2"
"manual_seed": 0, // manual seed for random & torch.random
"num_epochs": 1000, // number of epochs to train
// skipping batch that has large error for stable training
"skip_threshold": 3,
// split the batch into smaller chunks for less GPU memory consumption during training
"split_batch": 1,
// how many epochs to wait before saving a new checkpoint during training
"save_ckp_step": 1,
// how many epochs to wait before saving visual results during training (only works when option "save_image" is true)
"save_vis_step": 4,
// pre-train mode: null (from scratch) | "resume" (resume from specific checkpoint) | "finetune" (finetune a new model based on a specific model)
"pretrain": null,
// path to *.pth (required by "resume"/"finetune")
"pretrained_path": "./experiments/SRFBN_in3f32_x4/epochs/last_ckp.pth",
// weights for multiple losses (only works when option "use_cl" is true)
"cl_weights": [1.0, 1.0, 1.0, 1.0]
}
}